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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Gender Classification from Pose-Based GEIs
verfasst von : Raúl Martín-Félez, Ramón A. Mollineda, J. Salvador Sánchez
Erschienen in: Computer Vision and Graphics
Verlag: Springer Berlin Heidelberg
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This paper introduces a new approach for gait-based gender classification in which some key biomechanical poses of a gait pattern are represented by partial Gait Energy Images (GEIs). These pose-based GEIs can more accurately represent the shape of the body parts and some dynamic features with respect to the usually blurred depiction provided by a general GEI comprising all poses. Gait-based gender classification is based on the weighted decision fusion of the pose-based GEIs. Results of experiments on two large gait databases prove that this method performs significantly better than clasiffiers based on the original GEI.